Replication 1
## # A tibble: 2 x 5
## term estimate std.error statistic p.value
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 (Intercept) -1.42 0.641 -2.22 0.0574
## 2 mortality$`Grp 1 Dead` 0.00446 0.00338 1.32 0.224
## # A tibble: 10 x 9
## mortality..Log.~ mortality..Grp.~ .fitted .se.fit .resid .hat .sigma
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 0 24 -1.31 0.575 1.31 0.274 1.02
## 2 -3 13 -1.36 0.605 -1.64 0.303 0.910
## 3 -2 27 -1.30 0.566 -0.699 0.266 1.13
## 4 -1.30 179 -0.624 0.353 -0.677 0.104 1.14
## 5 -1 295 -0.107 0.575 -0.893 0.274 1.10
## 6 -0.602 259 -0.267 0.483 -0.335 0.194 1.17
## 7 -0.301 195 -0.553 0.367 0.252 0.112 1.17
## 8 0 294 -0.111 0.572 0.111 0.271 1.17
## 9 0.398 144 -0.780 0.351 1.18 0.102 1.08
## 10 0.699 165 -0.686 0.348 1.39 0.100 1.04
## # ... with 2 more variables: .cooksd <dbl>, .std.resid <dbl>
## # A tibble: 1 x 11
## r.squared adj.r.squared sigma statistic p.value df logLik AIC BIC
## <dbl> <dbl> <dbl> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
## 1 0.179 0.0760 1.10 1.74 0.224 2 -14.0 34.0 34.9
## # ... with 2 more variables: deviance <dbl>, df.residual <int>
## 1 2 3 4 5 6
## -1.3144841 -1.3634981 -1.3011166 -0.6238326 -0.1069579 -0.2673673
## 7 8 9 10
## -0.5525395 -0.1114137 -0.7797861 -0.6862140
## # A tibble: 2 x 5
## term estimate std.error statistic p.value
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 (Intercept) -1.42 0.641 -2.22 0.0574
## 2 mortality$`Grp 1 Dead` 0.00446 0.00338 1.32 0.224
##
## Call:
## glm(formula = factor(mortality$`Log Concentration (micromolar)`) ~
## mortality$`Grp 1 Dead`, family = binomial(link = "logit"))
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -5.176e-05 2.100e-08 2.100e-08 2.100e-08 6.247e-05
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -68.278 39632.039 -0.002 0.999
## mortality$`Grp 1 Dead` 3.681 1956.504 0.002 0.998
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 6.5017e+00 on 9 degrees of freedom
## Residual deviance: 6.5811e-09 on 8 degrees of freedom
## AIC: 4
##
## Number of Fisher Scoring iterations: 25
## 1 2 3 4 5
## 1.000000e+00 1.339598e-09 1.000000e+00 1.000000e+00 1.000000e+00
## 6 7 8 9 10
## 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## # A tibble: 2 x 5
## term estimate std.error statistic p.value
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 (Intercept) -68.3 39632. -0.00172 0.999
## 2 mortality$`Grp 1 Dead` 3.68 1957. 0.00188 0.998
## Dose SE
## p = 0.5: 113927309 2919.175
## Concentration Mortality Fitted Predicted
## 1 0.00000 24 -1.3144841 1.000000e+00
## 2 -3.00000 13 -1.3634981 1.339598e-09
## 3 -2.00000 27 -1.3011166 1.000000e+00
## 4 -1.30103 179 -0.6238326 1.000000e+00
## 5 -1.00000 295 -0.1069579 1.000000e+00
## 6 -0.60206 259 -0.2673673 1.000000e+00
## 7 -0.30103 195 -0.5525395 1.000000e+00
## 8 0.00000 294 -0.1114137 1.000000e+00
## 9 0.39794 144 -0.7797861 1.000000e+00
## 10 0.69897 165 -0.6862140 1.000000e+00
### Replication 2
## # A tibble: 2 x 5
## term estimate std.error statistic p.value
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 (Intercept) -1.60 0.744 -2.15 0.0640
## 2 mortality$`Grp 2 Dead` 0.00341 0.00253 1.35 0.215
## # A tibble: 10 x 9
## mortality..Log.~ mortality..Grp.~ .fitted .se.fit .resid .hat .sigma
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 0 115 -1.20 0.504 1.20 0.213 1.05
## 2 -3 105 -1.24 0.523 -1.76 0.229 0.891
## 3 -2 79 -1.33 0.574 -0.672 0.275 1.13
## 4 -1.30 151 -1.08 0.442 -0.219 0.164 1.17
## 5 -1 426 -0.144 0.545 -0.856 0.248 1.11
## 6 -0.602 435 -0.114 0.562 -0.488 0.264 1.15
## 7 -0.301 441 -0.0931 0.575 -0.208 0.276 1.17
## 8 0 336 -0.451 0.396 0.451 0.131 1.16
## 9 0.398 260 -0.710 0.346 1.11 0.100 1.08
## 10 0.699 251 -0.741 0.347 1.44 0.100 1.02
## # ... with 2 more variables: .cooksd <dbl>, .std.resid <dbl>
## # A tibble: 1 x 11
## r.squared adj.r.squared sigma statistic p.value df logLik AIC BIC
## <dbl> <dbl> <dbl> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
## 1 0.185 0.0828 1.09 1.81 0.215 2 -14.0 33.9 34.8
## # ... with 2 more variables: deviance <dbl>, df.residual <int>
## 1 2 3 4 5 6
## -1.20485325 -1.23895485 -1.32761902 -1.08208747 -0.14429335 -0.11360190
## 7 8 9 10
## -0.09314094 -0.45120779 -0.71037998 -0.74107142
## # A tibble: 2 x 5
## term estimate std.error statistic p.value
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 (Intercept) -1.60 0.744 -2.15 0.0640
## 2 mortality$`Grp 2 Dead` 0.00341 0.00253 1.35 0.215
##
## Call:
## glm(formula = factor(mortality$`Log Concentration (micromolar)`) ~
## mortality$`Grp 2 Dead`, family = binomial(link = "logit"))
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.63995 0.01658 0.09181 0.38937 1.00505
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.47261 3.81599 -0.386 0.700
## mortality$`Grp 2 Dead` 0.02396 0.03320 0.722 0.471
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 6.5017 on 9 degrees of freedom
## Residual deviance: 4.4526 on 8 degrees of freedom
## AIC: 8.4526
##
## Number of Fisher Scoring iterations: 8
## 1 2 3 4 5 6 7
## 0.7828483 0.7393864 0.6034647 0.8951784 0.9998388 0.9998701 0.9998875
## 8 9 10
## 0.9986094 0.9914732 0.9894432
## # A tibble: 2 x 5
## term estimate std.error statistic p.value
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 (Intercept) -1.47 3.82 -0.386 0.700
## 2 mortality$`Grp 2 Dead` 0.0240 0.0332 0.722 0.471
## Dose SE
## p = 0.5: 61.47117 82.61537
## Concentration Mortality Fitted Predicted
## 1 0.00000 115 -1.20485325 0.7828483
## 2 -3.00000 105 -1.23895485 0.7393864
## 3 -2.00000 79 -1.32761902 0.6034647
## 4 -1.30103 151 -1.08208747 0.8951784
## 5 -1.00000 426 -0.14429335 0.9998388
## 6 -0.60206 435 -0.11360190 0.9998701
## 7 -0.30103 441 -0.09314094 0.9998875
## 8 0.00000 336 -0.45120779 0.9986094
## 9 0.39794 260 -0.71037998 0.9914732
## 10 0.69897 251 -0.74107142 0.9894432
### Replication 3
## # A tibble: 2 x 5
## term estimate std.error statistic p.value
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 (Intercept) -2.02 0.976 -2.07 0.0724
## 2 mortality$`Grp 3 Dead` 0.00381 0.00266 1.43 0.190
## # A tibble: 10 x 9
## mortality..Log.~ mortality..Grp.~ .fitted .se.fit .resid .hat .sigma
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 0 161 -1.41 0.594 1.41 0.302 0.965
## 2 -3 300 -0.875 0.361 -2.12 0.111 0.781
## 3 -2 221 -1.18 0.472 -0.823 0.191 1.10
## 4 -1.30 137 -1.50 0.647 0.196 0.358 1.15
## 5 -1 425 -0.399 0.405 -0.601 0.141 1.13
## 6 -0.602 415 -0.437 0.392 -0.165 0.131 1.15
## 7 -0.301 458 -0.273 0.459 -0.0279 0.180 1.16
## 8 0 418 -0.426 0.396 0.426 0.134 1.14
## 9 0.398 350 -0.685 0.342 1.08 0.100 1.07
## 10 0.699 547 0.0661 0.641 0.633 0.352 1.12
## # ... with 2 more variables: .cooksd <dbl>, .std.resid <dbl>
## # A tibble: 1 x 11
## r.squared adj.r.squared sigma statistic p.value df logLik AIC BIC
## <dbl> <dbl> <dbl> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
## 1 0.204 0.104 1.08 2.05 0.190 2 -13.9 33.7 34.6
## # ... with 2 more variables: deviance <dbl>, df.residual <int>
## 1 2 3 4 5 6
## -1.40520816 -0.87538535 -1.17650767 -1.49668836 -0.39892599 -0.43704274
## 7 8 9 10
## -0.27314072 -0.42560772 -0.68480161 0.06609834
## # A tibble: 2 x 5
## term estimate std.error statistic p.value
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 (Intercept) -2.02 0.976 -2.07 0.0724
## 2 mortality$`Grp 3 Dead` 0.00381 0.00266 1.43 0.190
##
## Call:
## glm(formula = factor(mortality$`Log Concentration (micromolar)`) ~
## mortality$`Grp 3 Dead`, family = binomial(link = "logit"))
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.1168 0.3862 0.4043 0.5067 0.5904
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.264622 2.720932 0.465 0.642
## mortality$`Grp 3 Dead` 0.002877 0.008213 0.350 0.726
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 6.5017 on 9 degrees of freedom
## Residual deviance: 6.3774 on 8 degrees of freedom
## AIC: 10.377
##
## Number of Fisher Scoring iterations: 5
## 1 2 3 4 5 6 7
## 0.8491421 0.8935808 0.8699519 0.8400808 0.9232629 0.9211993 0.9297257
## 8 9 10
## 0.9218236 0.9065078 0.9447251
## # A tibble: 2 x 5
## term estimate std.error statistic p.value
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 (Intercept) 1.26 2.72 0.465 0.642
## 2 mortality$`Grp 3 Dead` 0.00288 0.00821 0.350 0.726
## Dose SE
## p = 0.5: -439.4975 2156.9
## Concentration Mortality Fitted Predicted
## 1 0.00000 161 -1.40520816 0.8491421
## 2 -3.00000 300 -0.87538535 0.8935808
## 3 -2.00000 221 -1.17650767 0.8699519
## 4 -1.30103 137 -1.49668836 0.8400808
## 5 -1.00000 425 -0.39892599 0.9232629
## 6 -0.60206 415 -0.43704274 0.9211993
## 7 -0.30103 458 -0.27314072 0.9297257
## 8 0.00000 418 -0.42560772 0.9218236
## 9 0.39794 350 -0.68480161 0.9065078
## 10 0.69897 547 0.06609834 0.9447251
### Replication 4
## # A tibble: 2 x 5
## term estimate std.error statistic p.value
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 (Intercept) -1.83 0.561 -3.26 0.0116
## 2 mortality$`Grp 4 Dead` 0.00311 0.00133 2.34 0.0473
## # A tibble: 10 x 9
## mortality..Log.~ mortality..Grp.~ .fitted .se.fit .resid .hat .sigma
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 0 58 -1.65 0.497 1.65 0.284 0.673
## 2 -3 100 -1.52 0.454 -1.48 0.236 0.764
## 3 -2 77 -1.59 0.477 -0.411 0.262 0.981
## 4 -1.30 125 -1.44 0.429 0.139 0.211 0.996
## 5 -1 486 -0.317 0.340 -0.683 0.132 0.958
## 6 -0.602 598 0.0311 0.433 -0.633 0.215 0.960
## 7 -0.301 527 -0.190 0.370 -0.111 0.157 0.996
## 8 0 524 -0.199 0.367 0.199 0.155 0.994
## 9 0.398 555 -0.103 0.393 0.501 0.177 0.975
## 10 0.699 545 -0.134 0.384 0.833 0.170 0.936
## # ... with 2 more variables: .cooksd <dbl>, .std.resid <dbl>
## # A tibble: 1 x 11
## r.squared adj.r.squared sigma statistic p.value df logLik AIC BIC
## <dbl> <dbl> <dbl> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
## 1 0.407 0.333 0.933 5.49 0.0473 2 -12.4 30.8 31.7
## # ... with 2 more variables: deviance <dbl>, df.residual <int>
## 1 2 3 4 5 6
## -1.64851954 -1.51788093 -1.58942112 -1.44011986 -0.31724997 0.03111964
## 7 8 9 10
## -0.18972181 -0.19905314 -0.10262941 -0.13373384
## # A tibble: 2 x 5
## term estimate std.error statistic p.value
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 (Intercept) -1.83 0.561 -3.26 0.0116
## 2 mortality$`Grp 4 Dead` 0.00311 0.00133 2.34 0.0473
##
## Call:
## glm(formula = factor(mortality$`Log Concentration (micromolar)`) ~
## mortality$`Grp 4 Dead`, family = binomial(link = "logit"))
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.73192 0.09545 0.10795 0.51351 0.83912
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.331796 1.744215 0.190 0.849
## mortality$`Grp 4 Dead` 0.009154 0.012421 0.737 0.461
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 6.5017 on 9 degrees of freedom
## Residual deviance: 4.7871 on 8 degrees of freedom
## AIC: 8.7871
##
## Number of Fisher Scoring iterations: 7
## 1 2 3 4 5 6 7
## 0.7032346 0.7768202 0.7382091 0.8139813 0.9916806 0.9969999 0.9942691
## 8 9 10
## 0.9941105 0.9955592 0.9951355
## # A tibble: 2 x 5
## term estimate std.error statistic p.value
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 (Intercept) 0.332 1.74 0.190 0.849
## 2 mortality$`Grp 4 Dead` 0.00915 0.0124 0.737 0.461
## Dose SE
## p = 0.5: -36.24467 229.8498
## Concentration Mortality Fitted Predicted
## 1 0.00000 58 -1.64851954 0.7032346
## 2 -3.00000 100 -1.51788093 0.7768202
## 3 -2.00000 77 -1.58942112 0.7382091
## 4 -1.30103 125 -1.44011986 0.8139813
## 5 -1.00000 486 -0.31724997 0.9916806
## 6 -0.60206 598 0.03111964 0.9969999
## 7 -0.30103 527 -0.18972181 0.9942691
## 8 0.00000 524 -0.19905314 0.9941105
## 9 0.39794 555 -0.10262941 0.9955592
## 10 0.69897 545 -0.13373384 0.9951355
### Replication 5
## # A tibble: 2 x 5
## term estimate std.error statistic p.value
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 (Intercept) -2.07 0.581 -3.55 0.00746
## 2 mortality$`Grp 5 Dead` 0.00389 0.00146 2.66 0.0289
## # A tibble: 10 x 9
## mortality..Log.~ mortality..Grp.~ .fitted .se.fit .resid .hat .sigma
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 0 260 -1.05 0.307 1.05 0.121 0.843
## 2 -3 46 -1.89 0.523 -1.11 0.351 0.786
## 3 -2 108 -1.65 0.449 -0.355 0.259 0.931
## 4 -1.30 154 -1.47 0.398 0.165 0.204 0.941
## 5 -1 409 -0.473 0.293 -0.527 0.110 0.920
## 6 -0.602 548 0.0681 0.405 -0.670 0.210 0.900
## 7 -0.301 638 0.419 0.508 -0.720 0.332 0.883
## 8 0 499 -0.123 0.356 0.123 0.163 0.942
## 9 0.398 483 -0.185 0.342 0.583 0.150 0.913
## 10 0.699 335 -0.761 0.280 1.46 0.100 0.743
## # ... with 2 more variables: .cooksd <dbl>, .std.resid <dbl>
## # A tibble: 1 x 11
## r.squared adj.r.squared sigma statistic p.value df logLik AIC BIC
## <dbl> <dbl> <dbl> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
## 1 0.469 0.403 0.883 7.07 0.0289 2 -11.8 29.7 30.6
## # ... with 2 more variables: deviance <dbl>, df.residual <int>
## 1 2 3 4 5 6
## -1.05339872 -1.88672863 -1.64529660 -1.46616961 -0.47318303 0.06809201
## 7 8 9 10
## 0.41855786 -0.12271718 -0.18502222 -0.76134384
## # A tibble: 2 x 5
## term estimate std.error statistic p.value
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 (Intercept) -2.07 0.581 -3.55 0.00746
## 2 mortality$`Grp 5 Dead` 0.00389 0.00146 2.66 0.0289
##
## Call:
## glm(formula = factor(mortality$`Log Concentration (micromolar)`) ~
## mortality$`Grp 5 Dead`, family = binomial(link = "logit"))
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.714e-05 2.100e-08 2.100e-08 2.100e-08 3.344e-05
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -53.645 58245.152 -0.001 0.999
## mortality$`Grp 5 Dead` 0.694 656.473 0.001 0.999
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 6.5017e+00 on 9 degrees of freedom
## Residual deviance: 1.8550e-09 on 8 degrees of freedom
## AIC: 4
##
## Number of Fisher Scoring iterations: 25
## 1 2 3 4 5
## 1.000000e+00 3.683763e-10 1.000000e+00 1.000000e+00 1.000000e+00
## 6 7 8 9 10
## 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## # A tibble: 2 x 5
## term estimate std.error statistic p.value
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 (Intercept) -53.6 58245. -0.000921 0.999
## 2 mortality$`Grp 5 Dead` 0.694 656. 0.00106 0.999
## Dose SE
## p = 0.5: 77.30064 29268.99
## Concentration Mortality Fitted Predicted
## 1 0.00000 260 -1.05339872 1.000000e+00
## 2 -3.00000 46 -1.88672863 3.683763e-10
## 3 -2.00000 108 -1.64529660 1.000000e+00
## 4 -1.30103 154 -1.46616961 1.000000e+00
## 5 -1.00000 409 -0.47318303 1.000000e+00
## 6 -0.60206 548 0.06809201 1.000000e+00
## 7 -0.30103 638 0.41855786 1.000000e+00
## 8 0.00000 499 -0.12271718 1.000000e+00
## 9 0.39794 483 -0.18502222 1.000000e+00
## 10 0.69897 335 -0.76134384 1.000000e+00
### Replication 6
## # A tibble: 2 x 5
## term estimate std.error statistic p.value
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 (Intercept) -1.57 0.578 -2.72 0.0263
## 2 mortality$`Grp 6 Dead` 0.00254 0.00141 1.80 0.110
## # A tibble: 10 x 9
## mortality..Log.~ mortality..Grp.~ .fitted .se.fit .resid .hat .sigma
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 0 55 -1.43e+0 0.515 1.43e+0 0.254 0.896
## 2 -3 114 -1.28e+0 0.453 -1.72e+0 0.197 0.819
## 3 -2 61 -1.42e+0 0.509 -5.83e-1 0.248 1.06
## 4 -1.30 126 -1.25e+0 0.442 -4.97e-2 0.187 1.09
## 5 -1 334 -7.23e-1 0.323 -2.77e-1 0.100 1.09
## 6 -0.602 653 8.81e-2 0.550 -6.90e-1 0.289 1.05
## 7 -0.301 616 -5.95e-3 0.508 -2.95e-1 0.247 1.09
## 8 0 618 -8.69e-4 0.510 8.69e-4 0.249 1.09
## 9 0.398 346 -6.92e-1 0.324 1.09e+0 0.100 1.00
## 10 0.699 464 -3.92e-1 0.369 1.09e+0 0.130 1.000
## # ... with 2 more variables: .cooksd <dbl>, .std.resid <dbl>
## # A tibble: 1 x 11
## r.squared adj.r.squared sigma statistic p.value df logLik AIC BIC
## <dbl> <dbl> <dbl> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
## 1 0.288 0.199 1.02 3.23 0.110 2 -13.3 32.6 33.5
## # ... with 2 more variables: deviance <dbl>, df.residual <int>
## 1 2 3 4 5
## -1.431755693 -1.281804875 -1.416506457 -1.251306403 -0.722666232
## 6 7 8 9 10
## 0.088084801 -0.005952153 -0.000869074 -0.692167760 -0.392266124
## # A tibble: 2 x 5
## term estimate std.error statistic p.value
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 (Intercept) -1.57 0.578 -2.72 0.0263
## 2 mortality$`Grp 6 Dead` 0.00254 0.00141 1.80 0.110
##
## Call:
## glm(formula = factor(mortality$`Log Concentration (micromolar)`) ~
## mortality$`Grp 6 Dead`, family = binomial(link = "logit"))
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.82409 0.09227 0.21940 0.53611 0.79681
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.547193 1.682679 0.325 0.745
## mortality$`Grp 6 Dead` 0.007951 0.010176 0.781 0.435
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 6.5017 on 9 degrees of freedom
## Residual deviance: 5.1614 on 8 degrees of freedom
## AIC: 9.1614
##
## Number of Fisher Scoring iterations: 7
## 1 2 3 4 5 6 7
## 0.7280014 0.8105555 0.7373447 0.8247746 0.9609424 0.9967933 0.9957011
## 8 9 10
## 0.9957687 0.9643701 0.9857483
## # A tibble: 2 x 5
## term estimate std.error statistic p.value
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 (Intercept) 0.547 1.68 0.325 0.745
## 2 mortality$`Grp 6 Dead` 0.00795 0.0102 0.781 0.435
## Dose SE
## p = 0.5: -68.81935 283.2402
## Concentration Mortality Fitted Predicted
## 1 0.00000 55 -1.431755693 0.7280014
## 2 -3.00000 114 -1.281804875 0.8105555
## 3 -2.00000 61 -1.416506457 0.7373447
## 4 -1.30103 126 -1.251306403 0.8247746
## 5 -1.00000 334 -0.722666232 0.9609424
## 6 -0.60206 653 0.088084801 0.9967933
## 7 -0.30103 616 -0.005952153 0.9957011
## 8 0.00000 618 -0.000869074 0.9957687
## 9 0.39794 346 -0.692167760 0.9643701
## 10 0.69897 464 -0.392266124 0.9857483